• 统计研究中心
当前位置: 首页> 系列讲座> 正文

加州大学伯克利分校丁鹏副教授:Interpretable sensitivity analysis for the Baron-Kenny approach to mediation with unmeasured confounding

主 题:Interpretable sensitivity analysis for the Baron-Kenny approach to mediation with unmeasured confounding

讲人:加州大学伯克利分校丁鹏副教授

主持人:统计学院林华珍教授

时间:2022年10月12日(周三)上午10:30-11:30

直播平台及会议ID:腾讯会议,894-226-616

主办单位:统计研究中心和统计学院 科研处


主讲人简介:

丁鹏,加州大学伯克利分校统计学系副教授。他于20155月获得哈佛大学统计学系博士学位,并于201512月在哈佛大学公共卫生学院流行病学学系做博士后。在此之前,他从北京大学取得经济学和数学学士学位和统计学硕士学位。他的研究兴趣包括统计因果推断,缺失数据,贝叶斯统计,应用统计。

内容提要:

Mediation analysis assesses the extent to which the treatment affects the outcome indirectly through a mediator and the extent to which it operates directly through other pathways. As the most popular method in empirical mediation analysis, the Baron-Kenny approach estimates the indirect and direct effects of the treatment on the outcome based on linear structural equation models. However, when the treatment and the mediator are not randomized, the estimates may be biased due to unmeasured confounding among the treatment, mediator, and outcome. Building on Cinelli and Hazlett (2020), we propose a sharp and interpretable sensitivity analysis method for the Baron-Kenny approach to mediation in the presence of unmeasured confounding. We first modify their omitted-variable bias formula to facilitate the discussion with heteroskedasticity and model misspecification. We then apply the result to develop a sensitivity analysis method for the Baron-Kenny approach. To ensure interpretability, we express the sensitivity parameters in terms of the partial R2's that correspond to the natural factorization of the joint distribution of the direct acyclic graph for mediation analysis. They measure the proportions of variability explained by unmeasured confounding given the observed variables. Moreover, we extend the method to deal with multiple mediators, based on a novel matrix version of the partial R2 and a general form of the omitted-variable bias formula. Importantly, we prove that all our sensitivity bounds are attainable and thus sharp.

中介分析评估了治疗通过中介间接影响结果的程度,以及治疗通过其他途径直接作用的程度。Baron-Kenny方法是实证中介分析中最常用的方法,它基于线性结构方程模型来估计治疗对结果的间接和直接影响。然而,当治疗和中介因素非随机化时,由于治疗、中介因素和结果之间的未测混杂,估计可能存在偏差。在CinelliHazlett(2020)的基础上,我们提出了一种尖锐的、可解释的敏感性分析方法,用于在存在不可测混杂的情况下使用Baron-Kenny方法进行调解。我们首先修改了他们的省略变量偏差公式,以便于讨论异方差和模型错定。然后将结果应用于Baron-Kenny方法的灵敏度分析方法。为了保证可解释性,我们将敏感性参数表示为与中介分析的直接无环图的联合分布的自然因式分解相对应的偏R2。他们测量在给定的观测变量下,由未测混杂解释的可变性的比例。此外,基于偏R2的新矩阵版本和省略变量偏置公式的一般形式,我们将该方法扩展到处理多个介质。重要的是,我们证明了我们所有的灵敏度界限都是可以达到的,而且精准。




上一条:宾夕法尼亚大学苏炜杰副教授:Gaussian Differential Privacy and Some Computational Challenges

下一条:耶鲁大学马双鸽教授:Hierarchical Cancer Heterogeneity Analysis Based On Histopathological Imaging Features